Currently there is a large infrastructure of existing two-dimensional image databases. These databases have been built over many years by private and public organizations. They represent a large investment of both time and money by the organization. They contain a great deal of information and are a valuable asset. One application for these two-dimensional databases is object recognition, and in particular, facial recognition.
Facial recognition is an important problem in computer vision, with applications to security, authentication, recognition, surveillance, multimedia, and other areas. Initially, a database (also known as a “gallery”) of known persons is generated. Then a sampled image (also known as a “probe”) is captured, for example, by a video camera. The image is then compared to the database to determine recognition (target-to-many matches) or authentication (target-to-one match). The need for a facial recognition system for security and other applications is taught by K. Bowyer, K. Chang, and P. Flynn in their paper “A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition” (Computer Vision and Understanding 101 [2006] 1-15).
The majority of the research and products for facial recognition involves the comparison of two-dimensional images. Two-dimensional comparison methods can use the large infrastructure of two-dimensional image databases currently available. These methods are well known and readily available.
Two-dimensional comparison methods also have many limitations, as is taught by Dionysian in U.S. Pat. No. 6,002,782. These limitations include the equipment and environment where the two-dimensional images are acquired. Differences in equipment produce different texture or type of images, for example between different still cameras, different video cameras, use of visible light, or infrared images. Differences in environment produce major differences in the images, for example lighting (time of day, brightness, lack of light, angle of available light) and the pose of the face.
It is commonly thought that because the shape of faces is not affected by obstacles, such as equipment or environment, three-dimensional comparisons have the potential to overcome many of the limitations of two-dimensional comparisons and have the potential for greater success. Three-dimensional models describe the shape of the object of interest. The model does not depend on the texture, type of image, lighting, or pose.
Practical implementations of fully three-dimensional recognition systems are inhibited by the fact that the existing reference databases are available only as two-dimensional images. Three-dimensional model databases do exist, but not to a large scale. They are not as well developed as existing two-dimensional image databases. Building a three-dimensional database is a large expense. Creating a three-dimensional model from scratch requires specialized equipment. Organizations would need to replace their current infrastructure of cameras and processors with new equipment. This equipment would also be a new expense for the organization. A larger obstacle to creating a three-dimensional model database from scratch is the need to re-sample all of the objects of interest. In the case of facial recognition, all of the people in the current image database would need to be re-sampled. While a company may be able to organize its employees to have their pictures re-taken, criminals and terrorists are less willing to sit for a law-enforcement organization or government organization to have their faces modeled.
Creating three-dimensional model databases from scratch is not an immediately feasible task. In many cases, the task may be prohibitive due to the expense or inability to re-sample all of the objects of interest. As a result, current work with three-dimensional models has primarily been directed towards using a three-dimensional probe model of a person of interest to produce a two-dimensional probe image. This two-dimensional probe image is compared to a two-dimensional database of known images.
There is therefore a need to provide a method and system to enable the use of the large infrastructure of existing two-dimensional facial databases for the purpose of three-dimensional facial recognition. There is an additional need for a method to enable comparisons of objects that is independent of the source used to derive the image of the object
The current embodiment provides such a method and system.